Adding incremental storage resources in a dispersed storage network

ABSTRACT

A method for execution by a computing device includes detecting that an incremental storage cohort has been added to a storage generation to produce an updated plurality of storage cohorts of an updated storage generation, where each storage cohort includes a set of storage units. For each storage cohort, a slice listing process is initiated to identify a plurality of DSN addresses associated with storage of data objects within the each storage cohort. For each DSN address, ranked scoring information is obtained for the each storage cohort of the updated plurality of storage cohorts. One storage cohort is identified based on the ranked scoring information. When the identified storage cohort is different than another storage cohort associated with current storage of encoded data slices associated with the DSN address of the identified storage cohort, a migration process is initiated to migrate the encoded data slices to the identified storage cohort.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation-in-part of U.S. Utility applicationSer. No. 15/006,735, entitled “MODIFYING STORAGE CAPACITY OF A SET OFSTORAGE UNITS”, filed Jan. 26, 2016, which claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/140,861,entitled “MODIFYING STORAGE CAPACITY OF A STORAGE UNIT POOL”, filed Mar.31, 2015, both of which are hereby incorporated herein by reference intheir entirety and made part of the present U.S. Utility PatentApplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention;

FIG. 10 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 11 is a logic diagram of an example of a method of addingincremental storage resources in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an embodiment of a decentralizedagreement module that includes a set of deterministic functions 1-N, aset of normalizing functions 1-N, a set of scoring functions 1-N, and aranking function. Each of the deterministic function, the normalizingfunction, the scoring function, and the ranking function, may beimplemented utilizing, for example, processing module 50 of thecomputing core 26. The decentralized agreement can may be implementedutilizing any module and/or unit of a dispersed storage network (DSN).For example, the decentralized agreement module can be implementedutilizing the distributed storage and task (DS) client module 34 of FIG.1 or any of the DST execution units of FIG. 9.

The decentralized agreement module functions to receive a ranked scoringinformation request and to generate ranked scoring information based onthe ranked scoring information request and other information. The rankedscoring information request includes one or more of an asset identifier(ID) of an asset associated with the request, an asset type indicator,one or more location identifiers of locations associated with the DSN,one or more corresponding location weights, and a requesting entity ID.The asset includes any portion of data associated with the DSN includingone or more asset types including a data object, a data record, anencoded data slice, a data segment, a set of encoded data slices, and aplurality of sets of encoded data slices. As such, the asset ID of theasset includes one or more of a data name, a data record identifier, asource name, a slice name, and a plurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSclient module 34 of FIG. 1, a computing device 16 of FIG. 1, anintegrity processing unit 20 of FIG. 1, a managing unit 18 of FIG. 1, acomputing device 12 of FIG. 1, and a computing device 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule to produce ranked scoring information with regards to selectionof a memory device of the set of memory devices for accessing aparticular encoded data slice (e.g., where the asset ID includes a slicename of the particular encoded data slice).

The decentralized agreement module outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module andthe second requesting entity identifies the first storage pool of theplurality of storage pools for retrieving the set of encoded data slicesutilizing the decentralized agreement module.

In an example of operation, the decentralized agreement module receivesthe ranked scoring information request. Each deterministic functionperforms a deterministic function on a combination and/or concatenation(e.g., add, append, interleave) of the asset ID of the request and anassociated location ID of the set of location IDs to produce an interimresult. The deterministic function includes at least one of a hashingfunction, a hash-based message authentication code function, a maskgenerating function, a cyclic redundancy code function, hashing moduleof a number of locations, consistent hashing, rendezvous hashing, and asponge function. As a specific example, deterministic function 2 appendsa location ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation. The ranking function includes rank ordering each score withother scores of the set of scores 1-N, where a highest score is rankedfirst. As such, a location associated with the highest score may beconsidered a highest priority location for resource utilization (e.g.,accessing, storing, retrieving, etc., the given asset of the request).Having generated the ranked scoring information, the decentralizedagreement module outputs the ranked scoring information to therequesting entity.

FIG. 10 is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the computing device 16 ofFIG. 1, the network 24 of FIG. 1, and at least one storage generation570. The computing device 16 includes a decentralized agreement module572 and the DST client module 34 of FIG. 1. The decentralized agreementmodule 572 may be implemented utilizing the decentralized agreementmodule 350 of FIG. 9. Each storage generation includes a plurality ofstorage cohorts (e.g., 1-C). Each storage cohort includes a set of DSTexecution (EX) units 1-n. Each DST execution unit includes a memory 84,which can be implemented by utilizing the memory 54 of FIG. 2. Each DSTexecution unit may be implemented utilizing the storage unit 36 ofFIG. 1. The DSN functions to add an incremental storage resource (e.g.,add an additional storage cohort C+1).

When a new cohort is added to expand a storage generation, it cantrigger a migration process. The migration process can be incorporatedinto a rebuilder process or can operate as a stand-alone process. Themigration process can operate by listing slices across each of thecohorts. For each source name returned by the listing, a determinationis can be made with the Distributed Agreement Protocol (DAP) inconjunction with the latest Storage Resource Map (which defines thestorage resources by each cohort in that generation). The DAP can be adeterministic algorithm which takes the source name and the StorageResource Map to return a cohort ID responsible for that source name. Ifthe storage unit that returned the slice is determined to no longer beresponsible for that source, then the migration process can perform atransfer of some or all the slices of that source to the responsiblestores on the new cohort. If at least a write-threshold number of slicescan be transferred, the transfer can be considered successful, and theslices on the originating storage cohort for that source are removed. Ifsome storage units are down or unavailable during the migration, therebuilder process can be used to recover those missing slices, and stillaffect their migration to their final destination (from as few as an IDAthreshold number of online storage units).

In an example of operation of adding the incremental storage resource,the DS client module 34 detects that an incremental storage cohort hasbeen added (e.g., has been added, should be added) to the storagegeneration to produce an updated plurality of storage cohorts (e.g., 1through C+1), where the storage generation includes one or more storagecohorts (e.g., previous storage cohorts). The detecting can include atleast one of receiving a command, interpreting a status response,interpreting an error message, interpreting updated system registryinformation, and/or determining to add the incremental storage cohort.

For each storage cohort of the one or more storage cohorts (e.g.,previous storage cohorts), the DS client module 34 can initiate a slicelisting process to identify a plurality of DSN addresses is associatedwith storage of a plurality of corresponding data objects within thestorage cohort. As a specific example, for each storage cohort, the DSclient module 34 issues, via the network 24, slice listing messages 578that includes list slice requests to the DST execution units associatedwith the storage cohort, receives, via the network 24, further slicelisting messages 578 that includes list slice responses, and extractsthe DSN addresses from the received list slice responses (e.g.,extracted a vault ID, a generation ID, and an object ID from slice namesto produce a source name).

For each DSN address, the DS client module 34 can obtain ranked scoringinformation for each storage cohort of the plurality of updated storagecohorts for the DSN address utilizing updated location weightsassociated with the storage cohort. For example, the DS client module 34issues a ranked scoring information request 574 to the decentralizedagreement module, where the request includes the DSN address,identifiers of the storage cohort, and a location weight of the storagecohort; and receives the ranked scoring information 576.

Having obtained the ranked scoring information 576, the DS client module34 can identify one storage cohort of the updated plurality of storagecohorts based on the rank scoring information. For example, the DSclient module 34 can identify a storage cohort associated with a highestscore as the identified one storage cohort. When the identified storagecohort is not a current storage cohort associated with storage ofencoded data slices 580 of the DSN address, the DS client module 34 caninitiate a migration process to migrate encoded data slices 580associated with the DSN address from the storage cohort to theidentified storage cohort. For example, the DS client module 34 issuesslice migration messages 582 to retrieve encoded data slices 580 fromthe storage cohort and issues further slice migration messages to sendthe retrieved encoded data slices 580 to the identified storage cohort.As another example, the DS client module 34 issues another slicemigration message to DST execution units associated with the encodeddata slices to be retrieved, where the slice migration messages includeinstructions to forward the encoded data slices to DST execution unitsassociated with the identified storage cohort.

In various embodiments, a processing system of a computing deviceincludes at least one processor and a memory that stores operationalinstructions, that when executed by the at least one processor cause theprocessing system to detect that an incremental storage cohort has beenadded to a storage generation to produce an updated plurality of storagecohorts of an updated storage generation, where each storage cohortincludes a set of storage units. For each storage cohort of the updatedplurality of storage cohorts, a slice listing process is initiated toidentify a plurality of DSN addresses associated with storage of aplurality of data objects within each storage cohort. For each DSNaddress of the plurality of DSN addresses, ranked scoring information isobtained for each storage cohort of the updated plurality of storagecohorts. One storage cohort of the updated plurality of storage cohortsis identified based on the ranked scoring information. When theidentified storage cohort is different than another storage cohortassociated with current storage of encoded data slices associated withthe DSN address of the identified storage cohort, a migration process isinitiated to migrate the encoded data slices from the other storagecohort to the identified storage cohort.

In various embodiments, the encoded data slices are associated with atleast one data segment, where the at least one data segment wasdispersed storage error encoded to produce the encoded data slices forstorage in the set of storage units. In various embodiments, thecomputing device detects that the incremental storage cohort has beenadded to the storage generation in response to determining to add theincremental storage cohort. In various embodiments, the computing devicedetermines to add the incremental storage cohort in response todetermining that an available storage level compares unfavorably to athreshold storage availability level.

In various embodiments, initiating the slice listing process includesissuing list slice requests for each storage cohort. List sliceresponses are received from each storage cohort and the plurality of DSNaddresses are extracted from the received list slice responses. Invarious embodiments, obtaining the ranked scoring information includesperforming a distributed agreement protocol function on the each DSNaddress utilizing an identifier of the each storage cohort and at leastone weight of the each storage cohort to produce the ranked scoringinformation for the each storage cohort. In various embodiments, the onestorage cohort is identified in response to determining the one storagecohort is associated with a highest score of the ranked scoringinformation. In various embodiments, initiating the migration processincludes retrieving the encoded data slices associated with the DSNaddress of the identified storage cohort from the other storage cohort.Storage of the retrieved encoded data slices in the identified storagecohort is facilitated.

FIG. 11 is a flowchart illustrating an example of adding an incrementalstorage resource. In particular, a method is presented for use inassociation with one or more functions and features described inconjunction with FIGS. 1-10, for execution by a computing device thatincludes a processor or via another processing system of a dispersedstorage network that includes at least one processor and memory thatstores instruction that configure the processor or processors to performthe steps described below.

The method includes step 590 where a processing system (e.g., of adistributed storage and task (DS) client module and/or a computingdevice) detects that an incremental storage cohort has been added to astorage generation to produce an updated plurality of storage cohorts ofan updated storage generation, where each storage cohort includes a setof storage units. The detecting can include at least one of receiving acommand, interpreting a query response, and/or determining to add theincremental storage cohort (e.g., when the additional storage capacityis required, for example, in response to comparing a storageavailability level to a storage availability threshold level anddetermining that the storage availability level compares unfavorably tothe storage availability threshold level).

For each storage cohort of the storage generation, the method continuesat step 592 where the processing system initiates a slice listingprocess to identify a plurality of DSN addresses associated with storageof a plurality of data objects within the storage cohort. For example,the processing system, for each storage cohort, issues list slicerequests, receives list slice responses, and extracts the DSN addressesfrom the received list slice responses.

For each DSN address, the method continues at step 594 where theprocessing system obtains ranked scoring information for each storagecohort of the updated plurality of storage cohorts. For example, theprocessing system performs a distributed agreement protocol function onthe DSN address utilizing an identifier of the storage cohort andweights of the storage cohort to produce the ranked scoring informationfor the storage cohort.

The method continues at step 596 where the processing system identifiesone storage cohort of the updated plurality of storage cohorts based onthe rank scoring information. For example, the processing systemidentifies a storage cohort associated with a highest score. When theidentified storage cohort is different than a storage cohort associatedwith current storage of encoded data slices associated with the DSNaddress, the method continues at step 598 where the processing systeminitiates a migration process to migrate the encoded data slices fromthe storage cohort to the identified storage cohort. For example, theprocessing system facilitates obtaining encoded data slices of the DSNaddress from the storage cohort and facilitate storage of the obtainedencoded data slices in the identified storage cohort (e.g., which mayinclude the original storage cohort or another storage cohort of theoriginal storage cohorts).

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to detect that an incremental storage cohort has beenadded to a storage generation to produce an updated plurality of storagecohorts of an updated storage generation, where each storage cohortincludes a set of storage units. For each storage cohort of the updatedplurality of storage cohorts, a slice listing process is initiated toidentify a plurality of DSN addresses associated with storage of aplurality of data objects within each storage cohort. For each DSNaddress of the plurality of DSN addresses, ranked scoring information isobtained for each storage cohort of the updated plurality of storagecohorts. One storage cohort of the updated plurality of storage cohortsis identified based on the ranked scoring information. When theidentified storage cohort is different than another storage cohortassociated with current storage of encoded data slices associated withthe DSN address of the identified storage cohort, a migration process isinitiated to migrate the encoded data slices from the other storagecohort to the identified storage cohort.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing system”, “processingmodule”, “processing circuit”, “processor”, and/or “processing unit” maybe used interchangeably, and may be a single processing device or aplurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing system, processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing system, processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing system, processing module, module, processing circuit, and/orprocessing unit includes more than one processing device, the processingdevices may be centrally located (e.g., directly coupled together via awired and/or wireless bus structure) or may be distributedly located(e.g., cloud computing via indirect coupling via a local area networkand/or a wide area network). Further note that if the processing system,processing module, module, processing circuit, and/or processing unitimplements one or more of its functions via a state machine, analogcircuitry, digital circuitry, and/or logic circuitry, the memory and/ormemory element storing the corresponding operational instructions may beembedded within, or external to, the circuitry comprising the statemachine, analog circuitry, digital circuitry, and/or logic circuitry.Still further note that, the memory element may store, and theprocessing system, processing module, module, processing circuit, and/orprocessing unit executes, hard coded and/or operational instructionscorresponding to at least some of the steps and/or functions illustratedin one or more of the Figures. Such a memory device or memory elementcan be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device thatincludes a processor, the method comprises: detecting that anincremental storage cohort has been added to a storage generation toproduce an updated plurality of storage cohorts of an updated storagegeneration, wherein each storage cohort includes a set of storage units;for each storage cohort of the updated plurality of storage cohorts,initiating a slice listing process to identify a plurality of DSNaddresses associated with storage of a plurality of data objects withinthe each storage cohort; for each DSN address of the plurality of DSNaddresses, obtaining ranked scoring information for the each storagecohort of the updated plurality of storage cohorts; identifying onestorage cohort of the updated plurality of storage cohorts based on theranked scoring information; and when the identified storage cohort isdifferent than another storage cohort associated with current storage ofencoded data slices associated with the DSN address of the identifiedstorage cohort, initiating a migration process to migrate the encodeddata slices from the another storage cohort to the identified storagecohort.
 2. The method of claim 1, wherein the encoded data slices areassociated with at least one data segment, and wherein the at least onedata segment was dispersed storage error encoded to produce the encodeddata slices for storage in the set of storage units.
 3. The method ofclaim 1, wherein the computing device detects that the incrementalstorage cohort has been added to the storage generation in response todetermining to add the incremental storage cohort.
 4. The method ofclaim 3, wherein the computing device determines to add the incrementalstorage cohort in response to determining that an available storagelevel compares unfavorably to a threshold storage availability level. 5.The method of claim 1, wherein initiating the slice listing processincludes: issuing list slice requests for the each storage cohort;receiving list slice responses from the each storage cohort; andextracting the plurality of DSN addresses from the received list sliceresponses.
 6. The method of claim 1, wherein obtaining the rankedscoring information includes performing a distributed agreement protocolfunction on the each DSN address utilizing an identifier of the eachstorage cohort and at least one weight of the each storage cohort toproduce the ranked scoring information for the each storage cohort. 7.The method of claim 1, wherein the one storage cohort is identified inresponse to determining the one storage cohort is associated with ahighest score of the ranked scoring information.
 8. The method of claim1, wherein initiating the migration process includes: retrieving theencoded data slices associated with the DSN address of the identifiedstorage cohort from the another storage cohort; and facilitating storageof the retrieved encoded data slices in the identified storage cohort.9. A processing system of a computing device comprises: at least oneprocessor; a memory that stores operational instructions, that whenexecuted by the at least one processor cause the processing system to:detect that an incremental storage cohort has been added to a storagegeneration to produce an updated plurality of storage cohorts of anupdated storage generation, wherein each storage cohort includes a setof storage units; for each storage cohort of the updated plurality ofstorage cohorts, initiate a slice listing process to identify aplurality of DSN addresses associated with storage of a plurality ofdata objects within the each storage cohort; for each DSN address of theplurality of DSN addresses, obtain ranked scoring information for theeach storage cohort of the updated plurality of storage cohorts;identify one storage cohort of the updated plurality of storage cohortsbased on the ranked scoring information; and when the identified storagecohort is different than another storage cohort associated with currentstorage of encoded data slices associated with the DSN address of theidentified storage cohort, initiate a migration process to migrate theencoded data slices from the another storage cohort to the identifiedstorage cohort.
 10. The processing system of claim 9, wherein theencoded data slices are associated with at least one data segment, andwherein the at least one data segment was dispersed storage errorencoded to produce the encoded data slices for storage in the set ofstorage units.
 11. The processing system of claim 9, wherein thecomputing device detects that the incremental storage cohort has beenadded to the storage generation in response to determining to add theincremental storage cohort.
 12. The processing system of claim 11,wherein the computing device determines to add the incremental storagecohort in response to determining that an available storage levelcompares unfavorably to a threshold storage availability level.
 13. Theprocessing system of claim 9, wherein initiating the slice listingprocess includes: issuing list slice requests for the each storagecohort; receiving list slice responses from the each storage cohort; andextracting the plurality of DSN addresses from the received list sliceresponses.
 14. The processing system of claim 9, wherein obtaining theranked scoring information includes performing a distributed agreementprotocol function on the each DSN address utilizing an identifier of theeach storage cohort and at least one weight of the each storage cohortto produce the ranked scoring information for the each storage cohort.15. The processing system of claim 9, wherein the one storage cohort isidentified in response to determining the one storage cohort isassociated with a highest score of the ranked scoring information. 16.The processing system of claim 9, wherein initiating the migrationprocess includes: retrieving the encoded data slices associated with theDSN address of the identified storage cohort from the another storagecohort; and facilitating storage of the retrieved encoded data slices inthe identified storage cohort.
 17. A computer readable storage mediumcomprises: at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to: detect that an incremental storage cohort has beenadded to a storage generation to produce an updated plurality of storagecohorts of an updated storage generation, wherein each storage cohortincludes a set of storage units; for each storage cohort of the updatedplurality of storage cohorts, initiate a slice listing process toidentify a plurality of DSN addresses associated with storage of aplurality of data objects within the each storage cohort; for each DSNaddress of the plurality of DSN addresses, obtain ranked scoringinformation for the each storage cohort of the updated plurality ofstorage cohorts; identify one storage cohort of the updated plurality ofstorage cohorts based on the ranked scoring information; and when theidentified storage cohort is different than another storage cohortassociated with current storage of encoded data slices associated withthe DSN address of the identified storage cohort, initiate a migrationprocess to migrate the encoded data slices from the another storagecohort to the identified storage cohort.
 18. The computer readablestorage medium of claim 17, wherein the encoded data slices areassociated with at least one data segment, and wherein the at least onedata segment was dispersed storage error encoded to produce the encodeddata slices for storage in the set of storage units.
 19. The computerreadable storage medium of claim 17, wherein obtaining the rankedscoring information includes performing a distributed agreement protocolfunction on the each DSN address utilizing an identifier of the eachstorage cohort and at least one weight of the each storage cohort toproduce the ranked scoring information for the each storage cohort. 20.The computer readable storage medium of claim 17, wherein the onestorage cohort is identified in response to determining the one storagecohort is associated with a highest score of the ranked scoringinformation.